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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Automated Transformation of GPU-Specific OpenCL Kernels Targeting Performance Portability on Multi-Core/Many-Core CPUs
verfasst von : Dafei Huang, Mei Wen, Changqing Xun, Dong Chen, Xing Cai, Yuran Qiao, Nan Wu, Chunyuan Zhang
Erschienen in: Euro-Par 2014 Parallel Processing
Verlag: Springer International Publishing
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When adapting GPU-specific OpenCL kernels to run on multi-core/many-core CPUs, coarsening the thread granularity is necessary and thus extensively used. However, locality concerns exposed in GPU-specific OpenCL code are usually inherited without analysis, which may give side-effects on the CPU performance. When executing GPU-specific kernels on CPUs, local-memory arrays no longer match well with the hardware and the associated synchronizations are costly. To solve this dilemma, we actively analyze the memory access patterns by using array-access descriptors derived from GPU-specific kernels, which can thus be adapted for CPUs by removing all the unwanted local-memory arrays together with the obsolete barrier statements. Experiments show that the automated transformation can satisfactorily improve OpenCL kernel performances on Sandy Bridge CPU and Intel’s Many-Integrated-Core coprocessor.